class_docstr: Random clipping
  
  For each deformation, the clip_limit parameter is drawn from a
  Beta distribution with parameters `(a, b)`
  
  This transformation affects the following attributes:
  - Audio
  
  Attributes
  ----------
  n_samples : int > 0
      The number of samples to generate per input
  
  a : float > 0.0
  b : float > 0.0
      Parameters of the Beta distribution from which
      clip_limit parameter is sampled.
  
  rng : None, int, or np.random.RandomState
      The random number generator state.
  
      If `None`, then `np.random` is used.
  
      If `int`, then `rng` becomes the seed for the random state.
  
  See Also
  --------
  Clipping
  LinearClipping
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code: def states(self, jam):
      clip_limits = self._rng.beta(
          a=self.a, b=self.b, size=self.n_samples
      )
  
      for clip_limit in clip_limits:
          yield dict(clip_limit=clip_limit)
  
file_path: muda/muda/deformers/clipping.py
imports: import numpy
  from ..base import BaseTransformer, _get_rng
label_desc: Use image node_id 2 for calling the RandomClipping obj's underlying member method code with example usage: obj.states(jam) without return types